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Hyperuricemia Prediction Using Photoplethysmogram and Arteriograph

Hafifah Ab Hamid1, Nazrul Anuar Nayan1,*, Mohd Zubir Suboh1, Nurin Izzati Mohamad Azizul1, Mohamad Nazhan Mohd Nizar1, Amilia Aminuddin2, Mohd Shahrir Mohamed Said3, Saharuddin Ahmad4

1 Department of Electrical, Electronic and Systems, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, 43600, Malaysia
2 Department of Physiology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Cheras, 56000, Malaysia
3 Department of Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Cheras, 56000, Malaysia
4 Family Medicine Department, Faculty of Medicine, Universiti Kebangsaan Malaysia, Cheras, 56000, Malaysia

* Corresponding Author: Nazrul Anuar Nayan. Email: email

(This article belongs to the Special Issue: Advances in Artificial Intelligence and Machine learning in Biomedical and Healthcare Informatics)

Computers, Materials & Continua 2022, 71(1), 287-304. https://doi.org/10.32604/cmc.2022.021987

Abstract

Hyperuricemia is an alarming issue that contributes to cardiovascular disease. Uric acid (UA) level was proven to be related to pulse wave velocity, a marker of arterial stiffness. A hyperuricemia prediction method utilizing photoplethysmogram (PPG) and arteriograph by using machine learning (ML) is proposed. From the literature search, there is no available papers found that relates PPG with UA level even though PPG is highly associated with vessel condition. The five phases in this research are data collection, signal preprocessing including denoising and signal quality indexes, features extraction for PPG and SDPPG waveform, statistical analysis for feature selection and classification of UA levels using ML. Adding PPG to the current arteriograph able to reduce cost and increase the prediction performance. PPG and arteriograph data were measured from 113 subjects, and 226 sets of data were collected from the left and right hands of the subjects. The performance of four types of ML, namely, artificial neural network (ANN), linear discriminant analysis (LDA), k-nearest neighbor (kNN), and support vector machine (SVM) in predicting hyperuricemia was compared. From the total of 98 features extracted, 16 features of which showed statistical significance for hyper and normouricemia. ANN gives the best performance compared to the other three ML techniques with 91.67%, 95.45%, and 94.12% for sensitivity, specificity, and accuracy, respectively. Features from PPG and arteriograph able to be used to predict hyperuricemia accurately and noninvasively. This study is the first to find the relationship of PPG with hyperuricemia. It shows a significant relation between PPG signals and arteriograph data toward the UA level. The proposed method of UA prediction shows its potential for noninvasive preliminary assessment.

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APA Style
Hamid, H.A., Nayan, N.A., Suboh, M.Z., Azizul, N.I.M., Nizar, M.N.M. et al. (2022). Hyperuricemia prediction using photoplethysmogram and arteriograph. Computers, Materials & Continua, 71(1), 287-304. https://doi.org/10.32604/cmc.2022.021987
Vancouver Style
Hamid HA, Nayan NA, Suboh MZ, Azizul NIM, Nizar MNM, Aminuddin A, et al. Hyperuricemia prediction using photoplethysmogram and arteriograph. Comput Mater Contin. 2022;71(1):287-304 https://doi.org/10.32604/cmc.2022.021987
IEEE Style
H.A. Hamid et al., "Hyperuricemia Prediction Using Photoplethysmogram and Arteriograph," Comput. Mater. Contin., vol. 71, no. 1, pp. 287-304. 2022. https://doi.org/10.32604/cmc.2022.021987



cc Copyright © 2022 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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